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English(EN) Differentiated Aggregation to Improve Generalization in Federated Learning

联邦学习算法FedALS降低通信成本

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为FedALS的新型联邦学习算法，旨在降低通信成本并提高模型泛化能力。该算法通过对模型的不同部分采用差异化的聚合频率来实现这一点，具体来说，对表示提取器（初始层）应用较低频率的聚合，而对头部（最终层）应用较高频率的聚合。研究论文中展示的实验结果表明，这种方法在非独立同分布（non-iid）场景下尤其有效。 AI

影响 这项研究可能带来更高效、更有效的联邦学习系统，尤其适用于去中心化数据集。

排序理由 该集群包含一篇详细介绍联邦学习新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

联邦学习算法FedALS降低通信成本

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该集群包含一篇详细介绍联邦学习新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Peyman Gholami, Hulya Seferoglu ·

    差异化聚合以提高联邦学习的泛化能力

    arXiv:2404.11754v4 Announce Type: replace Abstract: This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning ba…

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